Multifunctional central feeding and conveying system

Abstract

The invention relates to the technical field of industrial automation and intelligent manufacturing, and discloses a multifunctional central feeding conveying system which comprises a distributed intelligent feeding node module, each node is provided with an independent control unit and an independent sensor, and the distributed intelligent feeding node module is used for monitoring and adjusting material conveying parameters and detecting faults in real time through independent control and the sensors; collaboratively optimizing system operation; the Internet of Things gateway is used for transmitting the data of the feeding nodes to the central monitoring platform; the edge computing equipment is deployed at a feeding node and is used for processing local data; and the cloud computing platform is used for centralized data analysis and decision making. The distributed intelligent feeding node module is involved, and the independent control unit and various sensors are arranged, so that the system can detect and process faults, meanwhile, the edge computing equipment is deployed at the feeding node, data processing and preliminary analysis can be carried out locally, the burden of a central system is relieved, and the response speed is increased.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation and intelligent manufacturing, and in particular to a multifunctional central feeding and conveying system. Background Art

[0002] As the level of industrial automation continues to improve, material conveying systems on various production lines are also constantly developing to meet the increasingly complex production needs of different industrial fields. Traditional material conveying systems usually rely on centralized control, with slow system response speed and inability to adjust and adapt to changes in the production process in real time. These traditional systems also face problems such as unstable data transmission, untimely monitoring, and difficulty in precise control, which affects production efficiency and the stability of material transportation.

[0003] In a modern production environment, it is particularly important to manage the material conveying process in a timely and efficient manner. In order to solve these problems, advanced technologies such as edge computing technology, Internet of Things (IoT), cloud computing and big data analysis are gradually being introduced into the material conveying system. The application of these technologies not only improves the response speed and processing capacity of the system, but also enhances the system's intelligence and decision-making support capabilities.

[0004] In existing material handling systems, edge computing devices can perform data processing and preliminary analysis locally, reducing the burden on the central system and improving response speed. However, the modular design and flexibility of these systems are not perfect enough, making it difficult to quickly adapt to changing production needs. In addition, traditional user interfaces are often too complex and cumbersome to operate, making it difficult for users to efficiently manage and monitor the system.

[0005] In order to further improve the intelligence and efficiency of the material conveying system and meet the requirements of modern production for system flexibility, real-time and easy operation, a new solution is urgently needed. The present invention provides a multifunctional central feeding and conveying system. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a multifunctional central feeding and conveying system, which solves the problems of slow response speed, unstable data transmission, untimely monitoring, poor flexibility and complex operation of traditional material conveying systems.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multifunctional central feeding and conveying system, comprising:

[0008] Distributed intelligent feeding node module, each node is equipped with an independent control unit and sensor, which is used to monitor and adjust material conveying parameters in real time, detect faults, and coordinately optimize system operation through independent control and sensors;

[0009] IoT gateway, which is used to transmit data from the feeding nodes to the central monitoring platform;

[0010] Edge computing devices, which are deployed at feeder nodes and are used for local data processing;

[0011] Cloud computing platforms, which are used for centralized data analysis and decision making;

[0012] Intelligent scheduling system, which is used to adjust the feeding sequence and path based on machine learning and optimization algorithms;

[0013] Flexible production components with modular design to support fast switching and configuration of different materials;

[0014] Autonomous diagnosis and self-repair modules for equipment status monitoring and maintenance;

[0015] User interface, which is used for system operation and monitoring based on AR / VR technology;

[0016] Big data analysis platform for in-depth data analysis and feedback.

[0017] Preferably, the distributed intelligent feeding node module includes:

[0018] A control unit, which is used to execute control operations of the node and adjust material conveying parameters according to sensor data;

[0019] Flow sensors, which are used to monitor the flow rate of materials;

[0020] Density sensor, which is used to measure the density of materials and help adjust conveying parameters;

[0021] Temperature sensors, which are used to monitor material and ambient temperature;

[0022] Humidity sensor, which is used to monitor the ambient humidity;

[0023] The communication unit is connected to the control unit and is used to realize data communication and collaboration between the node and other nodes and the central system.

[0024] Preferably, the Internet of Things gateway includes:

[0025] A first data acquisition unit connected to the communication unit and used to collect data from the feeding nodes and sensors;

[0026] A data transmission unit, connected to the first data acquisition unit, for transmitting data to a central monitoring platform via a wireless or wired network;

[0027] Security protection unit, which is used to protect the security of data transmission and prevent data leakage or tampering;

[0028] The power management unit is connected to the security protection unit and the data transmission unit, and is used to manage the power supply of the gateway.

[0029] Preferably, the edge computing device includes:

[0030] Data processors, which are used to perform data processing and computing tasks;

[0031] A storage unit, which is used to cache local data;

[0032] A data analysis unit, which is used to perform preliminary analysis and processing on local data;

[0033] A communication interface for connecting the sensor and the control unit;

[0034] The fault management unit is connected to the data processor and is used for detecting and processing local equipment faults.

[0035] Preferably, the cloud computing platform includes:

[0036] A data storage unit, which is used to store a large amount of data generated by the system;

[0037] Data processing unit, which is used to perform high-performance computing and complex data processing tasks;

[0038] Analysis algorithm library, which is used to store various data analysis and machine learning algorithms;

[0039] A decision support unit, which is used to provide optimized decision suggestions based on the analysis results;

[0040] The security management unit is used to ensure the security of cloud data and computing processes.

[0041] Preferably, the intelligent scheduling system includes:

[0042] A scheduling algorithm unit, which is used to run a scheduling algorithm to optimize the feeding sequence and path;

[0043] A data input unit, which is used to obtain real-time and historical data for use by the scheduling algorithm;

[0044] A scheduling control unit, which is used to execute instructions generated by the scheduling algorithm;

[0045] Feedback mechanism unit, which is used to monitor the scheduling effect in real time and make adjustments.

[0046] Preferably, the modularly designed flexible production components include:

[0047] Quick-connect interface, which is used to quickly connect and disconnect components for easy adjustment and maintenance;

[0048] An adjustable conveying path module, which is connected to the material type identification unit and the configuration management unit, and is used to adjust the material conveying path according to production requirements;

[0049] Material type identification unit, which is used to automatically identify different types of materials and adjust conveying parameters;

[0050] The configuration management unit is used to manage and adjust system configuration to support various production requirements.

[0051] Preferably, the autonomous diagnosis and self-repair module includes:

[0052] A sensor unit for monitoring equipment status and operating parameters;

[0053] A diagnostic algorithm unit, which is used to analyze sensor data and diagnose device status;

[0054] A self-healing unit, which is used to perform repair operations, such as restarting or adjusting the device;

[0055] An alarm and notification unit is connected to the diagnostic algorithm unit and is used to sound an alarm and notify maintenance personnel when an abnormality is detected.

[0056] Preferably, the user interface includes:

[0057] A display unit for presenting system status and operation information;

[0058] An input unit, which is used to receive a control command input by a user;

[0059] Interactive software, which is used to realize human-computer interaction functions and support user operations;

[0060] Visualization tools, which provide data charts and models to help users understand the system status,

[0061] Preferably, the big data analysis platform includes:

[0062] A second data acquisition unit, which is connected to the distributed intelligent feeding node module, the Internet of Things gateway and other system components, and is used to collect data from various system components;

[0063] A data storage unit connected to the second data acquisition unit and the analysis engine, and used to store and manage a large amount of data;

[0064] An analysis engine, which is connected to the data storage unit and the report production unit, and is used to perform data analysis and mining tasks;

[0065] A report generation unit, which is used to generate analysis reports and provide system optimization suggestions;

[0066] A visualization unit connects the analysis engine and the report generation unit and is used to present the analysis results in charts and graphs.

[0067] Working principle: After the system is started, the distributed intelligent feeding node module is started first, and the independent control unit and sensor of each node start working to monitor the flow rate, density, temperature and humidity of the material in real time. The control unit uses the PID control algorithm to adjust the material conveying parameters in real time according to the sensor data to ensure the accuracy and stability of the conveying process. If an abnormality occurs, the system can quickly detect and handle the fault, thereby reducing downtime.

[0068] The IoT gateway collects data from the feeding node modules and transmits it to the central monitoring platform via wireless or wired networks. The first data acquisition unit ensures the comprehensiveness and real-time nature of the data, while the data transmission unit ensures the speed and stability of the transmission. The security protection unit protects the security of data transmission through measures such as data encryption and firewalls, while the power management unit ensures the stable operation of the gateway device.

[0069] At the local feeding node, the edge computing device processes and analyzes data to improve the system response speed. The data processor performs data processing tasks, the storage unit caches local data, and the data analysis unit performs preliminary analysis and provides immediate feedback. The communication interface ensures the fast transmission of data between sensors, control units and edge computing devices, and the fault management unit detects and handles local equipment faults in real time.

[0070] The cloud computing platform centrally processes and analyzes the system's data. The data storage unit stores the massive data generated by the system, the data processing unit performs high-performance computing, and the analysis algorithm library stores various data analysis and machine learning algorithms. The decision support unit provides optimization decision suggestions based on the analysis results, and the security management unit ensures the security of data and computing processes.

[0071] The intelligent scheduling system runs the scheduling algorithm unit based on real-time and historical data to optimize the feeding sequence and path. The data input unit obtains data to support scheduling decisions, the scheduling control unit executes scheduling instructions, and the feedback mechanism unit monitors the scheduling effect in real time and makes dynamic adjustments to ensure the efficiency and flexibility of the feeding process.

[0072] The modular design of flexible production components enables quick connection and disconnection of components through quick connection interfaces. The adjustable conveying path module adjusts the material conveying path according to the feedback data of the material type identification unit, and the configuration management unit dynamically adjusts the system configuration to support various production needs.

[0073] The autonomous diagnosis and self-repair module monitors the equipment status and operating parameters in real time through the sensor unit, and the diagnostic algorithm unit analyzes the sensor data to perform fault diagnosis. The self-repair unit automatically performs repair operations based on the diagnosis results, and the alarm and notification unit promptly notifies maintenance personnel when an abnormality is detected.

[0074] The user interface presents system status and operation information through the display unit, the input unit receives the user's control commands, and the interactive software supports the user's operation and management. Visualization tools provide intuitive data charts and models to help users understand the system status and operation, and improve management efficiency and decision-making quality.

[0075] The big data analysis platform collects data from each system component through the second data acquisition unit, the data storage unit stores and manages large amounts of data, and the analysis engine performs data analysis and mining tasks. The report generation unit generates detailed analysis reports and provides system optimization suggestions. The visualization unit intuitively presents the analysis results in charts and graphics to facilitate user understanding and application.

[0076] The present invention provides a multifunctional central feeding and conveying system, which has the following beneficial effects:

[0077] 1. The distributed intelligent feeding node module of the present invention is equipped with an independent control unit and a variety of sensors, which can monitor and adjust material conveying parameters in real time. Through independent control and sensors, the system can detect and handle faults, thereby improving the stability and efficiency of material conveying.

[0078] 2. By deploying edge computing devices at feeding nodes, the present invention can perform data processing and preliminary analysis locally, thereby reducing the burden on the central system and improving the response speed.

[0079] 3. The present invention supports the rapid switching and configuration of different materials through modularly designed flexible production components to meet changing production needs. The quick connection interface and adjustable conveying path module enable the system to flexibly adapt to production changes, automatically identify material types and adjust conveying parameters, thereby improving the adaptability and efficiency of the production line.

[0080] 4. The user interface of the present invention is based on AR / VR technology, which provides an intuitive system operation and monitoring method. The display unit and input unit enable users to easily view the system status and issue control commands. The interactive software and visualization tools help users understand complex system data, thereby improving the convenience of operation and management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a framework diagram of the system of the present invention;

[0082] Figure 2It is a framework diagram of the distributed intelligent feeding node module of the present invention;

[0083] Figure 3 This is a framework diagram of the Internet of Things gateway of the present invention;

[0084] Figure 4 It is a framework diagram of the edge computing device of the present invention;

[0085] Figure 5 This is a framework diagram of the cloud computing platform of the present invention;

[0086] Figure 6 It is a framework diagram of the intelligent scheduling system of the present invention;

[0087] Figure 7 A framework diagram of the flexible production assembly designed for modularization of the present invention;

[0088] Figure 8 It is a framework diagram of the autonomous diagnosis and self-repair module of the present invention;

[0089] Fig. 9 A framework diagram of the user interface of the present invention;

[0090] Fig.10 This is a framework diagram of the big data analysis platform of the present invention. DETAILED DESCRIPTION

[0091] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0092] Example:

[0093] Please refer to the attached Figure 1 The embodiment of the present invention provides a multifunctional central feeding and conveying system, comprising:

[0094] Distributed intelligent feeding node module, each node is equipped with an independent control unit and sensor, which is used to monitor and adjust material conveying parameters in real time, detect faults, and coordinately optimize system operation through independent control and sensors;

[0095] IoT gateway, which is used to transmit data from the feeding nodes to the central monitoring platform;

[0096] Edge computing devices, which are deployed at feeder nodes and are used for local data processing;

[0097] Cloud computing platforms, which are used for centralized data analysis and decision making;

[0098] Intelligent scheduling system, which is used to adjust the feeding sequence and path based on machine learning and optimization algorithms;

[0099] Flexible production components with modular design to support fast switching and configuration of different materials;

[0100] Autonomous diagnosis and self-repair modules for equipment status monitoring and maintenance;

[0101] User interface, which is used for system operation and monitoring based on AR / VR technology;

[0102] Big data analysis platform for in-depth data analysis and feedback.

[0103] Specifically, 1. The distributed intelligent feeding node module realizes real-time monitoring and intelligent adjustment of the material conveying process. Each node is equipped with an independent control unit and sensor to ensure the accuracy and stability of material conveying through real-time monitoring and independent control. This solves the problem of slow response, low efficiency and difficulty in real-time adjustment caused by centralized control in traditional systems.

[0104] This module specifically addresses the following:

[0105] The independent control unit adjusts the delivery parameters in real time according to the sensor data to improve the response speed.

[0106] Sensors (flow, density, temperature, humidity) monitor material status in real time to ensure the accuracy and stability of transportation.

[0107] The fault detection function can quickly find and handle abnormalities, reducing downtime.

[0108] 2. The IoT gateway ensures the timeliness and reliability of data transmission. The data of the feeding node is transmitted to the central monitoring platform to achieve efficient data exchange. This solves the problem of unstable data transmission in traditional systems, which affects real-time monitoring and decision-making.

[0109] This module specifically addresses the following:

[0110] The first data acquisition unit collects data from feeding nodes and sensors to ensure the integrity of the data.

[0111] The data transmission unit transmits data via wireless or wired network to ensure the speed and stability of transmission.

[0112] The security protection unit protects the security of data transmission and prevents data leakage or tampering.

[0113] The power management unit ensures stable operation of the gateway.

[0114] 3. Edge computing devices improve the timeliness and localization of data processing. Data is processed locally at the feeding node, reducing the burden on the central system and improving response speed. This solves the problem that traditional systems rely on central servers, resulting in high latency and heavy load.

[0115] This module specifically addresses the following:

[0116] Data processors perform local data processing and computing tasks, responding to demand in real time.

[0117] The storage unit caches local data to ensure the continuity of data processing.

[0118] The data analysis unit conducts preliminary data analysis and provides timely feedback.

[0119] The communication interface connects the sensor and the control unit to realize data exchange.

[0120] The fault management unit detects and handles local device failures to improve system reliability.

[0121] 4. The cloud computing platform realizes centralized data analysis and optimized decision-making. It centrally processes and analyzes the data of the entire system and provides optimization solutions. This solves the problem that the traditional system has weak data analysis capabilities and is difficult to provide optimized decisions.

[0122] This module specifically addresses the following:

[0123] The data storage unit stores the large amount of data generated by the system and ensures the integrity and availability of the data.

[0124] The data processing unit performs high-performance computing and handles complex data tasks.

[0125] The analytical algorithm library stores various data analysis and machine learning algorithms to provide support for optimization decisions.

[0126] The decision support unit provides optimization suggestions based on the analysis results to improve the system operation efficiency.

[0127] The security management unit ensures the security of cloud data and computing processes.

[0128] 5. The intelligent scheduling system optimizes the feeding sequence and path to improve system efficiency. Based on machine learning and optimization algorithms, the feeding path and sequence are dynamically adjusted. This solves the problem that the traditional system scheduling is inflexible and cannot cope with complex production needs.

[0129] This module specifically addresses the following:

[0130] The scheduling algorithm unit runs an intelligent scheduling algorithm to optimize the feeding sequence and path.

[0131] The data input unit obtains real-time and historical data to support scheduling decisions.

[0132] The dispatch control unit executes dispatch instructions to ensure the orderly progress of the feeding process.

[0133] The feedback mechanism unit monitors the scheduling effect in real time and makes adjustments based on actual conditions.

[0134] 6. The flexible production components with modular design support the rapid switching and configuration of various materials to adapt to changing production needs. Through modular design, the system can be flexibly configured and quickly adjusted. This solves the problem that traditional systems are difficult to quickly adapt to production changes and have poor flexibility.

[0135] This module specifically addresses the following:

[0136] The quick-connect interface enables quick connection and disconnection of components for easy adjustment and maintenance.

[0137] The adjustable conveying path module adjusts the material conveying path according to production needs to improve the adaptability of the system.

[0138] The material type recognition unit automatically identifies the material type and adjusts the conveying parameters to ensure the conveying effect.

[0139] The configuration management unit manages and adjusts system configuration to support various production requirements.

[0140] 7. The self-diagnosis and self-repair modules improve the system's self-maintenance capabilities and reduce manual intervention. They monitor the equipment status in real time and automatically diagnose and repair faults. This solves the problem that traditional systems rely on manual maintenance, which is slow to respond and costly.

[0141] This module specifically addresses the following:

[0142] Sensor units monitor equipment status and operating parameters, providing real-time data.

[0143] The diagnostic algorithm unit analyzes sensor data and performs equipment status diagnosis.

[0144] The self-healing unit performs repair operations, such as restarting or adjusting the device, to ensure that the system continues to operate.

[0145] The alarm and notification unit sounds an alarm when an abnormality is detected and notifies maintenance personnel.

[0146] 8. The user interface provides intuitive system operation and monitoring methods to improve management efficiency. AR / VR technology is used to achieve system operation and monitoring, which improves user experience. This solves the problem that traditional system operations are complex, non-intuitive, and affect efficiency.

[0147] This module specifically addresses the following:

[0148] The display unit presents system status and operating information, and displays data intuitively.

[0149] The input unit receives control commands input by the user to facilitate user operation.

[0150] The interactive software realizes the human-computer interaction function and supports users to manage and control the system.

[0151] Visualization tools provide data charts and models to help users understand system status and improve decision-making quality.

[0152] 9. The big data analysis platform provides system optimization suggestions through in-depth data analysis. It collects and analyzes system data and generates optimization reports and suggestions. It solves the problem of low data utilization and lack of in-depth analysis in traditional systems.

[0153] This module specifically addresses the following:

[0154] The second data acquisition unit collects data from each system component to ensure the comprehensiveness of the data.

[0155] The data storage unit stores and manages large amounts of data and provides a basis for analysis.

[0156] The analytical engine performs data analysis and mining tasks to discover potential problems and optimization space.

[0157] The report generation unit generates analysis reports and provides system optimization suggestions.

[0158] The visualization unit presents the analysis results in charts and graphs, making it easier for users to understand and apply them.

[0159] Please refer to the attached Figure 2 , the distributed intelligent feeding node module includes:

[0160] A control unit, which is used to execute control operations of the node and adjust material conveying parameters according to sensor data;

[0161] Flow sensors, which are used to monitor the flow rate of materials;

[0162] Density sensor, which is used to measure the density of materials and help adjust conveying parameters;

[0163] Temperature sensors, which are used to monitor material and ambient temperature;

[0164] Humidity sensor, which is used to monitor the ambient humidity;

[0165] The communication unit is connected to the control unit and is used to realize data communication and collaboration between the node and other nodes and the central system.

[0166] Specifically, the distributed intelligent feeding node module includes a control unit, a flow sensor, a density sensor, a temperature sensor, a humidity sensor and a communication unit. The module achieves high efficiency, stability and intelligence in the material conveying process through refined and intelligent control. The following are the beneficial effects, functions and existing technical problems solved by each part:

[0167] The control unit realizes precise control and intelligent management of material transportation. According to the sensor data, material transportation parameters are adjusted in real time. This solves the problem of slow response, untimely adjustment and inability to adapt to rapidly changing production needs caused by centralized control in traditional systems.

[0168] Use control algorithms to adjust material conveying parameters based on sensor data. For example, through PID control algorithms:

[0169] Among them, u(t) is the control input, e(t) is the error, K p ,K i ,K d are the proportional, integral and differential coefficients respectively.

[0170] The control unit uses the above formula to make real-time adjustments to ensure the stability and accuracy of material transportation.

[0171] 2. The flow sensor ensures that the material is evenly transported at the set rate. It monitors the flow rate of the material and provides real-time data to the control unit. This solves the problem of unstable transportation caused by the inability to monitor and adjust the material flow rate in real time in traditional systems.

[0172] This module specifically addresses the following:

[0173] The flow sensor measures the material flow rate in real time and transmits the data to the control unit.

[0174] The control unit adjusts the delivery parameters according to the flow data using the aforementioned PID control algorithm to keep the flow rate within the set range.

[0175] 3. Density sensors optimize the material conveying process and prevent blockages and uneven conveying. They measure the density of materials and help adjust conveying parameters. They solve the problem of uneven conveying or blockages caused by the inability to detect material density in real time in traditional systems.

[0176] This module specifically addresses the following:

[0177] The density sensor measures the material density in real time and transmits the data to the control unit.

[0178] The control unit dynamically adjusts the conveying parameters, such as flow rate and pressure, based on the density data to ensure a smooth conveying process.

[0179] 4. Temperature sensors ensure that materials are transported at suitable temperature conditions to prevent the temperature from affecting material properties. They monitor material and ambient temperatures and provide data to the control unit. This solves the problem in traditional systems where temperature fluctuations cannot be monitored and controlled in a timely manner, affecting material properties and transport efficiency.

[0180] This module specifically addresses the following:

[0181] The temperature sensor monitors the material and ambient temperature in real time, and the data is transmitted to the control unit.

[0182] The control unit adjusts the conveying parameters, such as heating or cooling measures, based on the temperature data to ensure that the material is conveyed at an appropriate temperature.

[0183] 5. Humidity sensor ensures that materials are transported under suitable humidity conditions to prevent humidity from affecting material properties. Monitors ambient humidity and provides data to the control unit. This solves the problem in traditional systems where humidity fluctuations cannot be monitored and controlled in a timely manner, affecting material properties and transport efficiency.

[0184] This module specifically addresses the following:

[0185] The humidity sensor monitors the ambient humidity in real time and transmits the data to the control unit.

[0186] The control unit adjusts the conveying parameters, such as humidification or dehumidification measures, based on the humidity data to ensure that the material is conveyed at an appropriate humidity.

[0187] 6. The communication unit realizes data communication and collaboration between nodes and other nodes and the central system, improving the overall coordination of the system. It connects with the control unit to realize data transmission and reception. It solves the problem of untimely data transmission in traditional systems, which affects the coordination and overall efficiency of the system.

[0188] This module specifically addresses the following:

[0189] The communication unit exchanges data with other nodes and the central system through wireless or wired networks.

[0190] Ensure that data transmission between nodes and the central system is timely and accurate, and enhance the overall collaborative working ability of the system.

[0191] Through real-time monitoring and adjustment of the above sensors and control units, combined with PID control algorithms, the system can respond in real time and optimize material conveying parameters. The efficient data exchange of the communication unit ensures the coordinated work of each node with the central system, forming a highly intelligent and flexible material conveying system. This is achieved in the following ways:

[0192] The control unit uses data from flow, density, temperature and humidity sensors to dynamically adjust material conveying parameters to ensure the stability of the conveying process.

[0193] The PID control algorithm is used for real-time adjustment. The proportional, integral and differential terms in the formula are calculated and fed back according to the sensor data to optimize the control effect.

[0194] The communication unit ensures real-time data transmission and collaboration, and the data exchange between nodes improves the overall efficiency and response speed of the system.

[0195] Please refer to the attached Figure 3 , the IoT gateway includes:

[0196] A first data acquisition unit connected to the communication unit and used to collect data from the feeding nodes and sensors;

[0197] A data transmission unit, connected to the first data acquisition unit, for transmitting data to a central monitoring platform via a wireless or wired network;

[0198] Security protection unit, which is used to protect the security of data transmission and prevent data leakage or tampering;

[0199] The power management unit is connected to the security protection unit and the data transmission unit, and is used to manage the power supply of the gateway.

[0200] Specifically, the IoT gateway is a key component of the multifunctional central feeding and conveying system, which includes a first data acquisition unit, a data transmission unit, a security protection unit, and a power management unit. Through efficient data acquisition, transmission, security protection, and power management, the gateway achieves reliable transmission and security protection of system data. The following are the beneficial effects, functions, and existing technical problems solved by each part:

[0201] The first data acquisition unit ensures the comprehensiveness and real-time nature of data collection. It collects data from feeding nodes and sensors to provide real-time system operation information. This solves the problem in traditional systems where data collection is incomplete and untimely, resulting in information lag and the inability to achieve real-time monitoring and decision-making.

[0202] Data collection rate R = data collection frequency f × number of sensors N

[0203] R=f×N

[0204] The first data acquisition unit collects a large amount of sensor data (such as flow, density, temperature, and humidity) at a high frequency to ensure the comprehensiveness and real-time nature of the data.

[0205] 2. The data transmission unit realizes fast and stable data transmission. The collected data is transmitted to the central monitoring platform through wireless or wired networks. This solves the problem of unstable data transmission and high latency in the system, which affects the overall coordination and efficiency of the system.

[0206] The data transmission unit uses a high-bandwidth network (such as 5G or optical fiber) for data transmission, reducing transmission delays.

[0207] Transmission protocol optimization (such as TCP / IP protocol optimization) ensures the stability and reliability of data transmission.

[0208] 3. The security protection unit protects the security of data transmission and prevents data leakage or tampering. It provides data encryption and security protection measures to ensure the security of data during transmission. It solves the problem of poor data transmission security in traditional systems, which is vulnerable to network attacks and data tampering, affecting system reliability.

[0209] Formula: Encryption algorithms (such as AES encryption) protect data. The formula is as follows:

[0210] C=E(K,P), where C is the ciphertext, E is the encryption algorithm, K is the key, and P is the plaintext.

[0211] The security protection unit uses the Advanced Encryption Standard (AES) to encrypt data and protect the transmitted data.

[0212] Use firewalls and intrusion detection systems (IDS) to monitor and protect against network attacks.

[0213] 3. The power management unit ensures the continuous and stable operation of the gateway device. It manages the power supply of the gateway and provides reliable power supply guarantee. It solves the problem in traditional systems that unstable power management may cause equipment failure or shutdown, affecting the continuity of data collection and transmission.

[0214] The power management unit uses an uninterruptible power supply (UPS) and backup batteries to ensure the stability of the power supply.

[0215] Monitor the power status in real time and automatically switch the power source to prevent equipment downtime due to power failure.

[0216] The relationship between the correlation formula and the technical solution

[0217] Through the collaborative work of the above units, the IoT gateway achieves efficient, secure and reliable data collection and transmission. This is achieved in the following ways:

[0218] The first data acquisition unit ensures high-frequency, multi-sensor data acquisition through the formula R=f×N to achieve comprehensive real-time monitoring.

[0219] The data transmission unit ensures fast and stable data transmission and reduces delays through high-bandwidth networks and optimized transmission protocols.

[0220] The security protection unit uses encryption algorithms (such as AES encryption) and network security measures to protect the security of data transmission and prevent leakage and tampering.

[0221] The power management unit uses UPS and backup batteries to monitor and manage power supply in real time to ensure continuous and stable operation of the gateway device.

[0222] Please refer to the attached Figure 4 , edge computing devices include:

[0223] Data processors, which are used to perform data processing and computing tasks;

[0224] A storage unit, which is used to cache local data;

[0225] A data analysis unit, which is used to perform preliminary analysis and processing on local data;

[0226] A communication interface for connecting the sensor and the control unit;

[0227] The fault management unit is connected to the data processor and is used for detecting and processing local equipment faults.

[0228] Specifically, edge computing devices play a key role in the multifunctional central feeding and conveying system, mainly including data processors, storage units, data analysis units, communication interfaces and fault management units. By processing and analyzing data locally at the feeding node, edge computing devices significantly improve the system's response speed and reliability.

[0229] 1. The data processor realizes fast data processing and calculation, improving the system response speed. It performs local data processing and calculation tasks to reduce the burden on the central system. It solves the problem that the traditional system relies on the central server, resulting in high data processing delay and slow response.

[0230] The data processor performs data processing tasks locally, reducing data transmission time and processing delays.

[0231] Utilize parallel computing technology to improve data processing efficiency and ensure real-time response.

[0232] Formula: Processing time T = data volume D / processing speed S

[0233] By increasing the processing speed S, the processing time T is reduced.

[0234] 2. The storage unit provides stable data cache to ensure the continuity of data processing. It caches local data to ensure the stability of data during transmission and processing. It solves the problem of data loss or delay during data transmission in traditional systems, which affects the continuity of processing.

[0235] The storage unit provides high-capacity, high-speed cache space to ensure the continuity and stability of data processing.

[0236] Use advanced caching technologies, such as NVMe SSD, to improve data access speed and reliability.

[0237] Cache hit rate H = number of hits C / total number of accesses A

[0238] By improving the cache hit rate H, data access latency is reduced.

[0239] 3. The data analysis unit performs preliminary data analysis locally and provides immediate feedback. It performs preliminary analysis and processing of local data to support quick decision-making. This solves the problem in traditional systems where data analysis relies on central servers, resulting in delayed feedback.

[0240] The data analysis unit performs preliminary data analysis locally and provides analysis results quickly.

[0241] Use machine learning and data mining techniques to improve the accuracy and efficiency of data analysis.

[0242] Analysis speed V = analysis data volume D / analysis time T

[0243] Improve analysis speed by optimizing algorithms and computing resources.

[0244] 4. The communication interface ensures fast data transmission between sensors, control units, and edge computing devices. It connects sensors and control units to achieve data exchange and communication. It solves the problems of low data transmission rate and poor communication efficiency in traditional systems.

[0245] The communication interface adopts high-speed interface standards (such as Ethernet, USB3.0) to ensure the data transmission rate.

[0246] Use reliable communication protocols (such as TCP / IP) to ensure the stability and reliability of data transmission.

[0247] Transmission rate R = data volume D / transmission time T

[0248] The transmission rate R is increased by improving the interface bandwidth and transmission efficiency.

[0249] 5. The fault management unit can quickly detect and handle equipment faults to ensure stable system operation. It can detect and handle local equipment faults to improve system reliability. It solves the problem of traditional systems where fault detection and handling rely on manual labor, and the response is slow and untimely.

[0250] The fault management unit monitors the equipment status through sensors and detects faults in real time.

[0251] Use fault diagnosis algorithms (such as fault tree analysis FTA) to quickly locate the cause of the fault.

[0252] Automatically perform repair operations (such as system restart and parameter adjustment) to ensure that the system returns to normal operation.

[0253] Fault detection rate F = number of faults detected D / total number of faults T

[0254] By increasing the fault detection rate F, system downtime is reduced.

[0255] Through the collaborative work of the above units, edge computing devices achieve efficient, secure and reliable data processing and analysis. This is achieved in the following ways:

[0256] The data processor uses the formula Ensure fast data processing, reduce latency and improve responsiveness.

[0257] The storage unit ensures the continuity and stability of data processing by improving the cache hit rate H.

[0258] The data analysis unit improves the analysis speed V and provides instant feedback by optimizing algorithms and computing resources.

[0259] The communication interface ensures fast data transmission by improving interface bandwidth and transmission efficiency.

[0260] The fault management unit ensures the stable operation and rapid recovery of the system by improving the fault detection rate F.

[0261] Please refer to the attached Figure 5 , cloud computing platform includes:

[0262] A data storage unit, which is used to store a large amount of data generated by the system;

[0263] Data processing unit, which is used to perform high-performance computing and complex data processing tasks;

[0264] Analysis algorithm library, which is used to store various data analysis and machine learning algorithms;

[0265] A decision support unit, which is used to provide optimized decision suggestions based on the analysis results;

[0266] The security management unit is used to ensure the security of cloud data and computing processes.

[0267] Specifically, the cloud computing platform plays a key role in centralized data processing and analytical decision-making in the multifunctional central feeding and conveying system, and mainly includes data storage unit, data processing unit, analytical algorithm library, decision support unit and security management unit. Through centralized, high-performance data processing and analysis, the platform significantly improves the intelligence and decision-making capabilities of the system.

[0268] 1. The data storage unit provides large-capacity and high-reliability data storage. It can store the massive data generated by the storage system and ensure the integrity and availability of the data. It solves the problem of limited data storage capacity, difficulty in processing large-scale data, and easy loss or damage of data in traditional systems.

[0269] Storage capacity C = number of storage media N × capacity of a single medium V

[0270] C=N×V

[0271] The data storage unit uses high-capacity storage media (such as HDD and SSD) to increase the total storage capacity by expanding the number of storage media.

[0272] Implement RAID technology to improve the reliability and redundancy of data storage.

[0273] 2. The data processing unit realizes high-performance computing and complex data processing, improving the system processing capacity. It performs high-performance computing and handles complex data analysis tasks. It solves the problem of insufficient data processing capacity and inability to quickly process and analyze large-scale data in traditional systems.

[0274] Processing capacity P = number of CPU cores C × single-core processing speed S

[0275] P=C×S

[0276] The data processing unit uses multi-core CPU and GPU to accelerate parallel processing and improve computing performance.

[0277] Use distributed computing frameworks (such as Hadoop and Spark) to handle large-scale data analysis tasks and improve processing efficiency.

[0278] 3. The analysis algorithm library provides a variety of data analysis and machine learning algorithms to support advanced data analysis. It stores and manages various data analysis and machine learning algorithms and provides tools for data processing. It solves the problem that the types of data analysis algorithms in traditional systems are limited and cannot meet complex analysis needs.

[0279] Algorithm performance A = data volume D / analysis time T

[0280] A=TD

[0281] The analysis algorithm library contains a variety of advanced algorithms (such as regression, classification, clustering, and deep learning) to enhance the breadth and depth of data analysis.

[0282] Use optimized algorithm implementation to improve algorithm performance and shorten analysis time.

[0283] 4. The decision support unit provides optimization decisions based on the analysis results to improve the decision-making ability of the system. Based on the data analysis results, it provides optimization suggestions and decision support. This solves the problem of insufficient decision support and inability to provide effective optimization solutions in a timely manner in traditional systems.

[0284] The decision support unit provides optimization suggestions based on the results of the analysis algorithm library combined with preset rules and models.

[0285] Use decision support tools such as decision trees and Bayesian networks to improve decision accuracy.

[0286] Decision accuracy D = number of correct decisions C / total number of decisions T

[0287] D=TC, by optimizing the decision-making model, the decision accuracy D is improved.

[0288] 4. The security management unit ensures the security of cloud data and computing processes, preventing data leakage and tampering. It provides data encryption and security management to protect the security of data and computing processes. It solves the problem of poor security of data and computing processes in traditional systems, which are vulnerable to attacks and tampering.

[0289] Security level S = encryption strength E × number of security measures N

[0290] S=E×N

[0291] The security management unit uses advanced encryption technology (such as AES-256) to protect data and ensure data transmission and storage security.

[0292] Implement multi-layer security protection measures (such as firewalls, intrusion detection systems) to improve the overall security level S.

[0293] Through the collaborative work of the above units, the cloud computing platform achieves efficient, secure and reliable data processing and analysis. This is achieved through the following methods:

[0294] The data storage unit provides large-capacity, high-reliability data storage through the formula C=N×V to ensure data integrity and availability.

[0295] The data processing unit improves computing performance through the formula P=C×S and handles complex data analysis tasks.

[0296] The analysis algorithm library uses the formula Provides a variety of data analysis and machine learning algorithms to support advanced data analysis.

[0297] The decision support unit uses the formula Provide optimized decision-making suggestions to improve the system's decision-making capabilities.

[0298] The security management unit improves the security of data and computing processes through the formula S=E×N to prevent data leakage and tampering.

[0299] Please refer to the attached Figure 6 , the intelligent scheduling system includes:

[0300] A scheduling algorithm unit, which is used to run a scheduling algorithm to optimize the feeding sequence and path;

[0301] A data input unit, which is used to obtain real-time and historical data for use by the scheduling algorithm;

[0302] A scheduling control unit, which is used to execute instructions generated by the scheduling algorithm;

[0303] Feedback mechanism unit, which is used to monitor the scheduling effect in real time and make adjustments.

[0304] Specifically, the intelligent scheduling system is one of the core modules of the multifunctional central feeding and conveying system, which mainly includes a scheduling algorithm unit, a data input unit, a scheduling control unit and a feedback mechanism unit. By optimizing the feeding sequence and path, the feeding process is made efficient, flexible and intelligent.

[0305] 1. The scheduling algorithm unit improves the optimization capability of the feeding process and reduces energy consumption and time costs. It runs an intelligent scheduling algorithm to optimize the feeding sequence and path. It solves the problem in traditional systems that the scheduling process relies on manual labor, is inefficient, and is difficult to cope with complex and dynamic production needs.

[0306] Optimize the objective function F through multi-objective optimization methods, such as:

[0307] Where T i is the time cost, E i For energy consumption.

[0308] Use intelligent optimization algorithms such as genetic algorithms and ant colony algorithms to improve scheduling efficiency.

[0309] The scheduling algorithm unit dynamically adjusts the feeding sequence and path based on real-time and historical data to reduce waiting time and energy consumption.

[0310] 3. The data input unit ensures that the scheduling algorithm has the latest and most comprehensive data support. It obtains and provides real-time and historical data for the scheduling algorithm to support scheduling decisions. It solves the problem of untimely and incomplete data acquisition in traditional systems, which affects the accuracy and effectiveness of scheduling decisions.

[0311] The data input unit obtains the status data and historical data of the current feeding system in real time through sensors and databases.

[0312] Ensure that the scheduling algorithm unit can make decisions based on the latest and comprehensive data when optimizing calculations.

[0313] 4. The scheduling control unit realizes the efficient execution of the optimization plan generated by the scheduling algorithm. According to the instructions generated by the scheduling algorithm, the feeding equipment is controlled to execute the scheduling plan. This solves the problem in the traditional system that the scheduling instructions are not executed in time, affecting the continuity and efficiency of the feeding process.

[0314] The dispatch control unit communicates with the feeding equipment through an interface to ensure the timely transmission and execution of dispatch instructions.

[0315] Use real-time control protocols (such as Modbus, CANbus) to improve the response speed and reliability of instruction execution.

[0316] 5. The feedback mechanism unit improves the adaptive ability and optimization effect of the scheduling system. It monitors the scheduling effect in real time and dynamically adjusts the scheduling strategy. It solves the problem in traditional systems that the scheduling process lacks feedback and adjustment mechanisms and is difficult to adapt to changing production conditions.

[0317] Feedback control mechanism ΔP=K p (RY), where ΔP is the adjustment amount, K p is the feedback gain, R is the expected value, and Y is the actual value.

[0318] The feedback mechanism unit monitors the actual operation effect of the feeding system in real time and compares it with the expected target.

[0319] Adjust the scheduling strategy based on the feedback data to ensure that the feeding process is always in an optimized state.

[0320] Through the collaborative work of the above units, the intelligent scheduling system realizes efficient optimization and intelligent management of the feeding process. This is achieved in the following ways:

[0321] The scheduling algorithm unit optimizes the objective function F and uses intelligent optimization algorithms such as genetic algorithms and ant colony algorithms to improve the optimization efficiency of the feeding sequence and path and reduce time and energy costs.

[0322] The data input unit acquires and provides the latest and comprehensive data in real time, supports the optimization calculation of the scheduling algorithm unit, and ensures the accuracy and effectiveness of decision-making.

[0323] The scheduling control unit ensures that the instructions generated by the scheduling algorithm are transmitted and executed in a timely manner through a real-time control protocol, thereby improving the continuity and efficiency of the feeding process.

[0324] The feedback mechanism unit uses the feedback control mechanism ΔP = K p (RY) monitors and adjusts scheduling strategies in real time to improve the system's adaptability and optimization effect.

[0325] Please refer to the attached Figure 7 , the modular design of flexible production components includes:

[0326] Quick-connect interface, which is used to quickly connect and disconnect components for easy adjustment and maintenance;

[0327] An adjustable conveying path module, which is connected to the material type identification unit and the configuration management unit, and is used to adjust the material conveying path according to production requirements;

[0328] Material type identification unit, which is used to automatically identify different types of materials and adjust conveying parameters;

[0329] The configuration management unit is used to manage and adjust system configuration to support various production requirements.

[0330] Specifically, the modularly designed flexible production component is an important module in the multifunctional central feeding and conveying system, which mainly includes a quick connection interface, an adjustable conveying path module, a material type identification unit, and a configuration management unit. This component significantly improves the adaptability and maintenance convenience of the system through modular and flexible design.

[0331] 1. The quick connection interface improves the flexibility and maintenance efficiency of the system. It enables quick connection and disconnection of components, making adjustment and maintenance easier. It solves the problem of complex component connection and disassembly, and time-consuming and labor-intensive maintenance and adjustment in traditional systems.

[0332] Connection time T = connection steps S × time per step t

[0333] T=S×t

[0334] The quick connection interface adopts standardized and modular design, reducing the number of connection steps S and the time t for each step, greatly shortening the connection time.

[0335] Use push-in fittings and quick-release mechanisms to quickly connect and disconnect components.

[0336] 2. The adjustable conveying path module dynamically adjusts the conveying path according to production needs to improve the flexibility of the production line. It connects the material type identification unit and the configuration management unit to adjust the material conveying path according to production needs. This solves the problem in traditional systems that the conveying path is fixed and difficult to adapt to changing production needs.

[0337] Path adjustment efficiency E = number of path adjustments N / total adjustment time T

[0338]

[0339] The adjustable conveying path module uses adjustable components such as electric valves and slide rails to quickly change the conveying path and improve the path adjustment efficiency E.

[0340] Combined with the data from the material type identification unit, the conveying path is automatically adjusted according to the material characteristics to ensure efficient transportation.

[0341] 2. The material type recognition unit automatically recognizes the material type and adjusts the conveying parameters to ensure the accuracy and stability of material conveying. It recognizes different types of materials and adjusts the conveying parameters according to the material characteristics. This solves the problem that material identification in traditional systems relies on manual labor, is inefficient and prone to errors.

[0342] Recognition accuracy R = number of correct recognitions C / total number of recognitions T

[0343]

[0344] The material type recognition unit uses sensors (such as spectral sensors, weight sensors) and image recognition technology to improve the recognition accuracy R.

[0345] The recognition results are directly fed back to the configuration management unit and the adjustable conveying path module to automatically adjust the conveying parameters and paths.

[0346] 3. The configuration management unit centrally manages and adjusts system configurations to support a variety of production requirements and improve system adaptability. It manages system configurations and adjusts system parameters according to material types and production requirements. This solves the problem in traditional systems where configuration adjustments are complex and time-consuming, and it is difficult to quickly respond to production changes.

[0347] Configuration adjustment time T = number of configuration items N × adjustment time per item t

[0348] T=N×t

[0349] The configuration management unit reduces the time t for each adjustment and improves the efficiency of configuration adjustment through software interface and automated control.

[0350] Combine real-time data and historical data to dynamically adjust system configuration to ensure that the system adapts to different production needs.

[0351] Through the collaborative work of the above units, the modularly designed flexible production components realize efficient, flexible and intelligent management of the system. This is achieved in the following ways:

[0352] The quick connection interface optimizes the connection steps and time through the formula T=S×t, realizes the rapid connection and disconnection of components, and improves the flexibility of the system.

[0353] The adjustable conveying path module is obtained by formula Improve the efficiency of path adjustment, dynamically adjust the conveying path according to production needs, and ensure efficient transportation.

[0354] The material type identification unit is determined by the formula Improve recognition accuracy, automatically identify material types and adjust conveying parameters to ensure conveying accuracy and stability.

[0355] The configuration management unit optimizes the configuration adjustment time through the formula T=N×t, improves the configuration adjustment efficiency, dynamically adjusts the system configuration according to production needs, and improves the system adaptability.

[0356] Please refer to the attached Figure 8 , the autonomous diagnosis and self-repair modules include:

[0357] A sensor unit for monitoring equipment status and operating parameters;

[0358] A diagnostic algorithm unit, which is used to analyze sensor data and diagnose device status;

[0359] A self-healing unit, which is used to perform repair operations, such as restarting or adjusting the device;

[0360] An alarm and notification unit is connected to the diagnostic algorithm unit and is used to sound an alarm and notify maintenance personnel when an abnormality is detected.

[0361] Specifically, the autonomous diagnosis and self-repair module plays a key role in the multifunctional central feeding and conveying system, mainly including the sensor unit, the diagnostic algorithm unit, the self-repair unit and the alarm and notification unit. Through the automated monitoring, diagnosis and repair functions, the module significantly improves the stability and maintenance efficiency of the system.

[0362] 1. The sensor unit realizes real-time monitoring of equipment status and operating parameters, improving the accuracy and timeliness of fault detection. It monitors equipment status and operating parameters and provides real-time data support. It solves the problem that in traditional systems, equipment status monitoring relies on manual or intermittent inspections and cannot achieve real-time monitoring.

[0363] Monitoring frequency F = sensor sampling frequency f × number of sensors N

[0364] F=f×N

[0365] The sensor unit uses a variety of sensors (such as temperature, pressure, and vibration sensors) to collect equipment status and operating parameter data at a high frequency, thereby increasing the monitoring frequency F.

[0366] Real-time data collection ensures the latest information on equipment status and operating parameters, and detects anomalies in a timely manner.

[0367] 2. The diagnostic algorithm unit improves the accuracy and speed of fault diagnosis and realizes intelligent analysis of equipment status. It analyzes sensor data and diagnoses equipment status. It solves the problem that fault diagnosis in traditional systems relies on experience or simple rules, making it difficult to locate problems quickly and accurately.

[0368] Diagnostic accuracy D = number of correct diagnoses C / total number of diagnoses T

[0369]

[0370] The diagnosis algorithm unit uses machine learning and data mining technology to establish equipment status models and fault diagnosis models to improve the diagnosis accuracy D.

[0371] Through historical data training and real-time data analysis, the cause of the fault can be quickly located and identified.

[0372] 4. The self-repair unit realizes automatic repair and adjustment of equipment, reduces manual intervention, and improves the continuous operation capability of the system. It performs repair operations such as restarting or adjusting equipment. It solves the problem in traditional systems that fault repair relies on manual operation, which is slow to respond and inefficient, and increases downtime.

[0373] The self-repair unit automatically performs repair operations based on the diagnosis results, such as restarting the device, adjusting operating parameters, or switching to a backup system, to ensure that the system quickly returns to normal operation.

[0374] Repair time T = fault response time R + repair execution time E

[0375] T=R+E By reducing the fault response time R and the repair execution time E, the total repair time T is shortened.

[0376] 4. The alarm and notification unit promptly notifies maintenance personnel of fault information to ensure efficient and timely maintenance work. When an abnormality is detected, an alarm is issued and maintenance personnel are notified. This solves the problem of untimely fault handling caused by an imperfect fault alarm and notification mechanism in traditional systems.

[0377] The alarm and notification unit notifies maintenance personnel of fault information in a timely manner through text messages, emails, alarm sounds, etc.

[0378] Notification response time N = fault detection time D + notification delivery time T

[0379] N=D+T, by reducing the fault detection time D and the notification delivery time T, the notification response time N is improved.

[0380] Through the collaborative work of the above units, the autonomous diagnosis and self-repair module realizes efficient, intelligent and automatic equipment status monitoring, diagnosis and repair. This is achieved through the following methods:

[0381] The sensor unit increases the monitoring frequency through the formula F=f×N, ensuring real-time data support for equipment status and operating parameters and timely detection of abnormalities.

[0382] The diagnostic algorithm unit uses the formula Use machine learning and data mining technology to improve diagnostic accuracy and quickly locate the cause of the fault.

[0383] The self-repair unit automatically performs repair operations through the formula T=R+E, reducing manual intervention and downtime and ensuring continuous operation of the system.

[0384] The alarm and notification unit improves the notification response time through the formula N=D+T, and promptly notifies maintenance personnel to handle the fault.

[0385] Please refer to the attached Fig. 9 , the user interface includes:

[0386] A display unit for presenting system status and operation information;

[0387] An input unit, which is used to receive a control command input by a user;

[0388] Interactive software, which is used to realize human-computer interaction functions and support user operations;

[0389] Visualization tools are used to provide data charts and models to help users understand the system status.

[0390] Specifically, the user interface plays a vital role in the multifunctional central feeding and conveying system, which mainly includes the display unit, input unit, interactive software and visualization tools. The user interface enables users to easily operate and monitor the system through intuitive and efficient human-computer interaction design, improving the convenience of operation and system management efficiency.

[0391] 1. The display unit provides clear and intuitive system status and operation information, improving the user's control over the system. It presents system status and operation information for users to view in real time. This solves the problem in traditional systems where information display is not intuitive and users find it difficult to fully understand the system status.

[0392] Display information coverage C = effective display information volume I e / Total information volume I t

[0393]

[0394] The display unit adopts a high-resolution display screen to ensure clear presentation of information and improve the display information coverage C.

[0395] Use multiple windows and hierarchical layout design to enable users to quickly locate and obtain key information.

[0396] 2. The input unit simplifies user input operations and improves the accuracy and response speed of control commands. It receives control commands input by users and realizes direct control of the system. It solves the problems of single and complex input methods, low user operation efficiency and easy errors in traditional systems.

[0397] Input accuracy A = number of correct input commands C / total number of input commands T

[0398]

[0399] The input unit includes multiple input methods, such as touch screen, keyboard and mouse, to improve the input accuracy A.

[0400] Provide shortcut keys and preset command options to simplify user operation processes and reduce input errors.

[0401] 3. The interactive software realizes smooth human-computer interaction, improves the user operation experience and the usability of the system. It supports user operation and realizes comprehensive control of system functions. It solves the problems of unfriendly human-computer interaction interface, complex operation and poor user experience in traditional systems.

[0402] The interactive software is user-friendly and easy to operate, and supports multiple interaction methods (such as clicking, dragging, and gestures).

[0403] Realize human-computer interaction functions such as real-time data update, operation confirmation, and system feedback to enhance user experience.

[0404] Interaction response time R = user operation time T u +System response time T s

[0405] R=T u +T s , by optimizing the user operation process and system response speed, the interactive response time R is reduced.

[0406] 4. Visualization tools provide intuitive data charts and models to help users quickly understand the system status and operation status. The data is presented in a graphical way to support user analysis and decision-making. This solves the problem in traditional systems where data presentation is single and users find it difficult to quickly understand and analyze system data.

[0407] Data visualization rate V = amount of visualized data D v / Total data volume D t

[0408]

[0409] Visualization tools provide a variety of charts and models (such as line charts, bar charts, scatter plots, and 3D models) to improve data visualization rate.

[0410] Dynamically refreshed and interactive charts enable users to track data changes and trends in real time and conduct in-depth analysis.

[0411] Through the collaborative work of the above units, the user interface realizes efficient, intuitive and intelligent human-computer interaction design. This is achieved through the following methods:

[0412] The display unit is expressed by the formula Improve display information coverage and provide clear and intuitive system status and operation information.

[0413] Input cells through the formula Improve input accuracy, simplify user input operations, and ensure the accuracy and response speed of control commands.

[0414] The interactive software uses the formula R = T u +T s Optimize user operation processes and system response speed to provide a smooth human-computer interaction experience.

[0415] Visualization tool through formula Improve data visualization rate and provide intuitive data charts and models to help users quickly understand system status and operation status.

[0416] Please refer to the attached Fig.10 , the big data analysis platform includes:

[0417] A second data acquisition unit, which is connected to the distributed intelligent feeding node module, the Internet of Things gateway and other system components, and is used to collect data from various system components;

[0418] A data storage unit connected to the second data acquisition unit and the analysis engine, and used to store and manage a large amount of data;

[0419] An analysis engine, which is connected to the data storage unit and the report production unit, and is used to perform data analysis and mining tasks;

[0420] A report generation unit, which is used to generate analysis reports and provide system optimization suggestions;

[0421] A visualization unit connects the analysis engine and the report generation unit and is used to present the analysis results in charts and graphs.

[0422] Specifically, the big data analysis platform plays a key role in the multifunctional central feeding and conveying system, mainly including the second data acquisition unit, data storage unit, analysis engine, report generation unit and visualization unit. Through efficient data acquisition, storage, analysis and visualization, the platform significantly improves the system's data processing capabilities and decision support capabilities.

[0423] 1. The second data acquisition unit realizes comprehensive collection of multi-source data, improving the integrity and real-time performance of the data. It connects the distributed intelligent feeding node module, the IoT gateway and other system components to collect data from each system component. It solves the problem of limited data collection scope and incomplete data source in traditional systems, which affects the accuracy of analysis results.

[0424] Data collection rate R = data collection frequency f × number of data sources N

[0425] R=f×N

[0426] The second data collection unit collects data from multiple data sources at a high frequency, thereby increasing the data collection rate R.

[0427] Distributed data collection technology is used to ensure the real-time and integrity of data.

[0428] 2. The data storage unit provides large-capacity, high-reliability data storage to ensure data persistence and availability. It stores and manages large amounts of data collected from various system components. It solves the problem of insufficient data storage capacity, low data management efficiency, and easy data loss in traditional systems. Specific solutions:

[0429] Storage capacity C = number of storage devices N × single device capacity V

[0430] C=N×V

[0431] The data storage unit uses high-capacity storage devices (such as HDD, SSD) and increases the total storage capacity C by expanding the number of storage devices.

[0432] Implement data redundancy and backup strategies to improve data reliability and durability.

[0433] 3. The analysis engine implements complex data analysis and mining tasks and supports deep data insights. It performs data analysis and mining tasks and connects the data storage unit and the report generation unit. It solves the problem of insufficient data analysis capabilities and inability to extract valuable information from big data in traditional systems.

[0434] Analysis performance P = number of analysis tasks T / analysis time t

[0435]

[0436] The analysis engine uses high-performance computing technology and distributed computing architecture to improve analysis performance P.

[0437] Use machine learning, statistical analysis, and data mining algorithms to gain deeper insights into your data.

[0438] 4. The report generation unit generates detailed analysis reports, provides system optimization suggestions, and supports decision-making. Generates analysis reports and provides system optimization suggestions. This solves the problem that the presentation of data analysis results in traditional systems is not intuitive and difficult to support decision-making.

[0439] The report generation unit automatically generates a detailed analysis report based on the results of the analysis engine.

[0440] Provide system optimization suggestions to help users make scientific decisions and improve system performance.

[0441] Report generation efficiency E = number of reports generated N / report generation time t

[0442] Improve report generation efficiency through automated report generation technologyE.

[0443] 4. The visualization unit intuitively presents the analysis results in the form of charts and graphs to improve data understanding and decision support capabilities. It connects the analysis engine and the report generation unit to present the analysis results in the form of charts and graphs.

[0444] Visualization coverage V = amount of visualized data D v / Total data volume D t

[0445]

[0446] The visualization unit provides a variety of charts and graphs (such as line charts, bar charts, scatter plots, and heat maps) to improve the visualization coverage V.

[0447] Dynamic and interactive visualization tools enable users to explore and analyze data in real time, improving data understanding and decision support capabilities.

[0448] Through the collaborative work of the above units, the big data analysis platform realizes efficient, comprehensive and intelligent data processing and analysis. This is achieved through the following methods:

[0449] The second data acquisition unit improves the data acquisition rate through the formula R=f×N, realizes the comprehensive acquisition of multi-source data, and ensures the real-time and integrity of the data.

[0450] The data storage unit provides large-capacity, high-reliability data storage through the formula C=N×V, ensuring data persistence and availability.

[0451] The analytical engine uses the formula Improve analytical performance, perform complex data analysis and mining tasks, and support deep data insights.

[0452] Report generation unit through the formula Improve report generation efficiency, generate detailed analysis reports, provide system optimization suggestions, and support decision making.

[0453] Visualization unit through the formula Improve visualization coverage, intuitively present analysis results in the form of charts and graphs, and enhance data understanding and decision support capabilities.

[0454] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multifunctional central feeding and conveying system, characterized in that: include: Distributed intelligent feeding node module, each node is equipped with an independent control unit and sensor, which is used to monitor and adjust material conveying parameters in real time, detect faults, and coordinately optimize system operation through independent control and sensors; IoT gateway, which is used to transmit data from the feeding nodes to the central monitoring platform; Edge computing devices, which are deployed at feeder nodes and are used for local data processing; Cloud computing platforms, which are used for centralized data analysis and decision making; Intelligent scheduling system, which is used to adjust the feeding sequence and path based on machine learning and optimization algorithms; Flexible production components with modular design to support fast switching and configuration of different materials; Autonomous diagnosis and self-repair modules for equipment status monitoring and maintenance; User interface, which is used for system operation and monitoring based on AR / VR technology; Big data analysis platform for in-depth data analysis and feedback.

2. A multifunctional central feeding and conveying system according to claim 1, characterized in that: The distributed intelligent feeding node module includes: A control unit, which is used to execute control operations of the node and adjust material conveying parameters according to sensor data; Flow sensors, which are used to monitor the flow rate of materials; Density sensor, which is used to measure the density of materials and help adjust conveying parameters; Temperature sensors, which are used to monitor material and ambient temperature; Humidity sensor, which is used to monitor the ambient humidity; The communication unit is connected to the control unit and is used to realize data communication and collaboration between the node and other nodes and the central system.

3. A multifunctional central feeding and conveying system according to claim 1, characterized in that: The Internet of Things gateway includes: A first data acquisition unit connected to the communication unit and used to collect data from the feeding nodes and sensors; A data transmission unit, connected to the first data acquisition unit, for transmitting data to a central monitoring platform via a wireless or wired network; Security protection unit, which is used to protect the security of data transmission and prevent data leakage or tampering; The power management unit is connected to the security protection unit and the data transmission unit, and is used to manage the power supply of the gateway.

4. A multifunctional central feeding and conveying system according to claim 1, characterized in that: The edge computing device comprises: Data processors, which are used to perform data processing and computing tasks; A storage unit, which is used to cache local data; A data analysis unit, which is used to perform preliminary analysis and processing on local data; A communication interface for connecting the sensor and the control unit; The fault management unit is connected to the data processor and is used for detecting and processing local equipment faults.

5. A multifunctional central feeding and conveying system according to claim 1, characterized in that: The cloud computing platform includes: A data storage unit, which is used to store a large amount of data generated by the system; Data processing unit, which is used to perform high-performance computing and complex data processing tasks; Analysis algorithm library, which is used to store various data analysis and machine learning algorithms; A decision support unit, which is used to provide optimized decision suggestions based on the analysis results; The security management unit is used to ensure the security of cloud data and computing processes.

6. A multifunctional central feeding and conveying system according to claim 1, characterized in that: The intelligent scheduling system comprises: A scheduling algorithm unit, which is used to run a scheduling algorithm to optimize the feeding sequence and path; A data input unit, which is used to obtain real-time and historical data for use by the scheduling algorithm; A scheduling control unit, which is used to execute instructions generated by the scheduling algorithm; Feedback mechanism unit, which is used to monitor the scheduling effect in real time and make adjustments.

7. A multifunctional central feeding and conveying system according to claim 1, characterized in that: The modular design of the flexible production components includes: Quick-connect interface, which is used to quickly connect and disconnect components for easy adjustment and maintenance; An adjustable conveying path module, which is connected to the material type identification unit and the configuration management unit, and is used to adjust the material conveying path according to production requirements; Material type identification unit, which is used to automatically identify different types of materials and adjust conveying parameters; The configuration management unit is used to manage and adjust system configuration to support various production requirements.

8. A multifunctional central feeding and conveying system according to claim 1, characterized in that: The autonomous diagnosis and self-repair module includes: A sensor unit for monitoring equipment status and operating parameters; A diagnostic algorithm unit, which is used to analyze sensor data and diagnose device status; A self-healing unit, which is used to perform repair operations, such as restarting or adjusting the device; An alarm and notification unit is connected to the diagnostic algorithm unit and is used to sound an alarm and notify maintenance personnel when an abnormality is detected.

9. A multifunctional central feeding and conveying system according to claim 1, characterized in that: The user interface comprises: A display unit for presenting system status and operation information; An input unit, which is used to receive a control command input by a user; Interactive software, which is used to realize human-computer interaction functions and support user operations; Visualization tools are used to provide data charts and models to help users understand the system status.

10. A multifunctional central feeding and conveying system according to claim 1, characterized in that: The big data analysis platform includes: A second data acquisition unit, which is connected to the distributed intelligent feeding node module, the Internet of Things gateway and other system components, and is used to collect data from various system components; A data storage unit connected to the second data acquisition unit and the analysis engine, and used to store and manage a large amount of data; An analysis engine, which is connected to the data storage unit and the report production unit, and is used to perform data analysis and mining tasks; A report generation unit, which is used to generate analysis reports and provide system optimization suggestions; A visualization unit connects the analysis engine and the report generation unit and is used to present the analysis results in charts and graphs.

Images